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Azure Synapse Analytics combines the power of Data Lakes with Data Warehouses, empowering the organizations to build Big Data, Advanced Analytics and Business Intelligence in one single platform.
Data lakes have been around for years yet there is still much hype and hyperbole surrounding their use. This session goes beyond corny puns and broken metaphors and instead provides real-world guidance.
How do you deploy multiple machine learning models in production to solve your challenge? How do enable canary releases and A/B testing? How do you make sure a user is always served by the same version of the model?
Together Power BI and Azure Synapse Analytics can handle huge datasets. This session details how the composite model and aggregation capabilities of Power BI can be used to fully exploit the scale provided by Synapse.
In this session we'll focus on the benefits of using Azure Data Lake. We will cover how we use ADLS in real projects to produce a security architecture which is adaptable, scalable and provides monitoring capabilities.
A client's case on how to build a robust metadata driven datahub by utilizing Databricks (Delta Lake) for data ingestion, validation, and loading
Azure Synapse Analytics combines the power of Data Lakes with Data Warehouses, empowering the organizations to build Big Data, Advanced Analytics and Business Intelligence in one single platform.
This session will cover how to create flexible, reliable, and scalable ETL and ELT pipelines across Azure using Azure Data Factory
We will see how to construct a cloud-first architecture based on serverless data analytics. We will look at specific challenges and cost saving strategies, to produce a reliable, scalable and cost effective solution!
Common Data Model as the foundation of Power BI Dataflows and as part of the Open Data Initiative with SAP and Adobe, seems to be a pretty good move from Microsoft. We want to take a closer look to this approach
Learn how Azure supports interactive, exploratory notebooks (e.g. Jupyter) for data processing and experimentation across a range of scales from simple single-computer work up to massively parallel Databricks clusters.
In this session we will merge practice and theory, analyzing what is data virtualization and data lake, what their benefits and how to implement them using SQL Server 2019 Big Data Cluster
Databricks, Lakes & Parquet are a match made in heaven, but explode with extra power when using Delta Lake. This session will dive into the details of how Databricks Delta works and how to make the most of it.
Deep dive into the Common Data Model world and learn how to deploy, extend and use it along with other Microsoft technologies like Power Apps, Power BI Dataflows and Azure Databricks.
Come learn how to design your enterprise-scale data lake on Azure using Azure Data Lake Storage
Delivering enterprise BI with Power BI Premium tabular cubes, Azure Databricks ETL and Delta Lake for transaction integrity
In this session we focus on how Spark implements Machine Learning at Scale with Spark ML.
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Azure's breadth of products can make technology selection a challenge. Learn how to make pragmatic and informed choices that meet your application's data transformation, processing and storage requirements.
Azure Databricks has become one of the staples of big data processing. See how to make the most of it by understanding how Spark works under the covers.
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